# Interference Removal for Radar/Communication Co-existence: the Random Scattering Case

## TL;DR

This paper addresses interference mitigation in spectrum sharing between radar and communication systems by proposing two algorithms that exploit sparsity to effectively remove radar-induced interference in uncoordinated scenarios.

## Contribution

It introduces two novel algorithms for interference removal in radar/communication coexistence, leveraging sparsity and addressing non-convex joint optimization problems.

## Key findings

- Two algorithms effectively mitigate interference in simulations.
- The two-stage alternating minimization achieves good performance with moderate complexity.
- Algorithms outperform baseline methods in interference suppression.

## Abstract

In this paper we consider an un-cooperative spectrum sharing scenario, wherein a radar system is to be overlaid to a pre-existing wireless communication system. Given the order of magnitude of the transmitted powers in play, we focus on the issue of interference mitigation at the communication receiver. We explicitly account for the reverberation produced by the (typically high-power) radar transmitter whose signal hits scattering centers (whether targets or clutter) producing interference onto the communication receiver, which is assumed to operate in an un-synchronized and un-coordinated scenario. We first show that receiver design amounts to solving a non-convex problem of joint interference removal and data demodulation: next, we introduce two algorithms, both exploiting sparsity of a proper representation of the interference and of the vector containing the errors of the data block. The first algorithm is basically a relaxed constrained Atomic Norm minimization, while the latter relies on a two-stage processing structure and is based on alternating minimization. The merits of these algorithms are demonstrated through extensive simulations: interestingly, the two-stage alternating minimization algorithm turns out to achieve satisfactory performance with moderate computational complexity.